Limiting parallelism for certain map operations in a Rayon parallel computation

[bradlarsen]

I have a secrets detection app, Nosey Parker, that uses Rayon for the bulk of its parallel execution.

The parallel computation, very roughly, is to find files on disk and scan them, all in parallel with as many cores as are available.

I have been looking more closely at the performance of this application, and I see that in most environments, even with fast SSDs, the optimal performance for reading the files from disk is to use a smaller number of parallel workers than the number of CPU cores (maybe 1/2). Going beyond this value burns lots of CPU time in the kernel without actually speeding anything up.

However, in this application, the actual scanning of files once read from disk is compute-bound, benefitting from as many cores as are available.

I have been staring at the Rayon docs and poking through the code, but have not seen an obvious way to limit the parallel computation's degree of parallelism for just the I/O part without also limiting parallelism for the compute-bound part.

Any guidance on how to achieve this?

[cuviper]

I don't think we have a way to do this in a single iterator chain, but you could probably use separate ThreadPools for this, one for I/O with limited threads that farms the compute out to a bigger pool.

[bradlarsen]

How would you go about connecting the outputs from the I/O threadpool to the bigger one — a pair of channels?

[bradlarsen]

Another thought I have had is to use a single thread pool with a mutex or similar to hold some number of tokens, and require that each I/O operation acquire a token. If the mutex cannot be locked, call rayon::yield_now and then retry. This approach I think could be used to limit parallelism around scarce resources in single Rayon iterator chains. Crazy idea?

[cuviper]

Beware of #592 if you try the mutex idea -- that is, you may hit a recursion deadlock if you make rayon calls while holding a mutex. I think it will be ok if you only yield when the lock fails though.

[bradlarsen]

Indeed! In the meantime, I have been experimenting with multiple explicit Rayon thread pools, explicitly connecting the output of one as the input to the next using channels. Seems to work well...

[bradlarsen]

Here is a blog post from 2020 about using multiple thread pools in Rayon: https://pkolaczk.github.io/multiple-threadpools-rust/. It's a bit old now but still relevant.

In that blog post, a separate ThreadPool is used for each hardware device (like a magnetic hard disk and an SSD). A map operation is run on each thread pool, and each of them writes its results to an std::sync::mpsc::Channel, which the main application thread reads from.

Notably, the multiple thread pools in this blog post all send their outputs to the same place — the shared std::sync::mpsc::Channel. The multiple thread pools are not sequentially connected to each other, with the output of one acting as the input of another.

Multiple thread pools could be connected sequentially using channels. But I wonder if there is a better way to do this in Rayon?

[adamreichold]

Multiple thread pools could be connected sequentially using channels. But I wonder if there is a better way to do this in Rayon?

I am not sure if I would call it decidedly better or worse, but an alternative approach to basically the same problem was recently discussed in #1173 (comment)